Comparing of performance of DPCA, RBD,SAM and LA methods in detection and separation of alteration haloes in porphyry copper resources by case study on region Meyduk of Kerman province

Since Iran is a rich country in copper resources, in order to track and introduce of prone mineral areas, using the applied methods with wide regional coverage such as satellite digital image data is inevitable thereby in addition to save time and cost, more potential region with acceptable precision for more accurate studies be elected.One of the most important cases which in preliminary step at satellite studies must be noted is environmental changes and surface effects that in related to these resources appear on surface of the earth which one of the most important is presence of alteration layers, how zones place related to each other, presence of Gossans layer and breccia zones
It is clear that according to diversity of remote sensing methods in studying of earth effects, all methods are not the same in terms of efficiency and performance and each method may be appropriate to introduce one effect of earth surface or alteration; accordingly, different methods of principal component analyzing, RBD,SAM and LA were used to compare efficiency and precision and performance of methods in introducing of alteration halos and Gossans.
Also, with consider to importance of faults and linear structures on mineralization and alteration of different rock unites, linear structures were extracted using filtration method in different directions. The results, showed that the best regions for deposition of porphyry copper are the area with high density of lineation and the best results are achieved when during the filtering in vertical direction on main forces and stresses of the region.Then, in order to evaluation of accuracy and comparison of methods, 92 samples were obtained from alteration zones and mineralization area.Accordingly, it is found that SAM for introducing of Phyllic alteration type and advanced argilic is the best method and DPCA has the highest performance to recognize propylitic and gossans region.